Power sector decarbonization plays a vital role in the upcoming energy transition towards a more sustainable future. Decentralized energy resources, such as Electric Vehicles (EV) and solar photovoltaic systems (PV), are continuously integrated in residential power systems, increasing the risk of bottlenecks in power distribution networks. This paper aims to address the challenge of domestic EV charging while prioritizing clean, solar energy consumption. Real Time-of-Use tariffs are treated as a price-based Demand Response (DR) mechanism that can incentivize end-users to optimally shift EV charging load in hours of high solar PV generation with the use of Deep Reinforcement Learning (DRL). Historical measurements from the Pecan Street dataset are analyzed to shape a flexibility potential reward to describe end-user charging preferences. Experimental results show that the proposed DQN EV optimal charging policy is able to reduce electricity bills by an average 11.5\% by achieving an average utilization of solar power 88.4
翻译:电力部门去碳化在即将到来的能源向更可持续的未来的能源过渡中发挥着关键作用。电力车辆和太阳能光伏发电系统等分散能源不断被纳入住宅电力系统,增加了电力分配网络瓶颈的风险。本文件旨在应对国内EV收费的挑战,同时优先考虑清洁的太阳能消费。实际使用时间关税被视为一种基于价格的需求反应机制,它可以鼓励终端用户利用深强化学习(DRL),在高太阳能光伏发电时最优化地转移EV充电负荷。对Pecan街数据集的历史测量进行了分析,以形成一种灵活性的潜在奖励,描述最终用户的收费偏好。实验结果表明,拟议的DQN EV最佳收费政策通过实现平均利用太阳能88.4,能够平均减少电费11.5 ⁇ 。